Systems involving large amounts of complex data, such as transaction networks and multipurpose sensor systems, are difficult to monitor effectively for some of the following reasons. First, these systems involve large numbers of different types of indicators. Second, the applicable indicators result from examination of the data being monitored. For example, in a transaction network involving many types of transactions, measuring the “average transaction dollar value” may only be applicable for financial transactions. Third, groups of indicators being monitored have different value ranges indicating whether their operation is “normal”, “good”, “bad”, etc. Fourth, groups of indicators being monitored have different “thresholds” indicating whether an external action is required.
As such, providing an interface that allows users to view diverse data that is changing in real-time is a challenging problem, especially when it is important that the user be able to quickly identify and react to an adverse change. In the case of many large distributed systems like computer networks, the problem is compounded by the fact that, upon seeing a change, the user must be able to quickly identify the source of the change.
In the following, several prior art methods and systems are briefly reviewed.
FIG. 1 is a screen capture illustrating an exemplary dashboard presentation 10 with widgets and logs in accordance with the prior art. Common practice in user interfaces for systems management is to provide a series of dashboards of various indicators such as duration, throughput or rate, and delay. These dashboards generally consist of widgets such as bar graphs, line graphs, pie charts, and logs in a layout configured by the application designer, system administrator, or end user. However, there are several problems with this approach. First, they may be very difficult to interpret at a glance. Second, drilling down to get more detailed information results in a switching of dashboards which makes it difficult to maintain context. Third, it is of limited use in complex monitoring situations as simply adding more dashboards to the screen is not feasible as each widget complicates the entire screen and there is a limited amount of screen space. Fourth, dashboards must be carefully constructed to ensure that users are provided with the information required for their specific purpose. This construction is problematic as it is difficult for the end user to know beforehand what visual information will be needed for problem identification and creating a new dashboard to get to the information needed is generally difficult or time consuming.
FIG. 2 is a screen capture illustrating an exemplary tableplot presentation 20 in accordance with the prior art. One way of examining a large amount of data on a single screen is a “tableplot”. A tableplot is a display that supplements each cell of a table with a symbol proportionate to the cell value. For example, black circles may be used for positive values and red diamonds may be used for negative values. In addition, each cell in the table may contain a circle or diamond where the size is proportionate to the cell value. However, while a cell may show two different values using concentric rings, it may not be updated over time. In addition, such tableplots are not interactive for drill down or for pulling up related statistics.
FIG. 3 is a screen capture illustrating an exemplary categorized bubble chart presentation 30 with two categories in accordance with the prior art. And, FIG. 4 is a screen capture illustrating an exemplary categorized bubble chart presentation 40 with one category and one free axis (vertical) in accordance with the prior art. Another way of examining a large amount of data on a single screen is the categorized bubble chart. As in the tableplot, data points in such a bubble chart are categorized, or appear in any location along a free axis. However, these bubble charts are not interactive nor do they allow for drill down to sub-categories.
FIG. 5 is a screen capture illustrating an exemplary two dimensional (“2D”) bubble chart presentation 50 in accordance with the prior art. And, FIG. 6 is a screen capture illustrating an exemplary three dimensional (“3D”) bubble chart presentation 60 in accordance with the prior art. Bubble charts display data with varying sized bubbles. The size of the bubble is dependent on a third variable. It is possible to change the bubble to any symbol with some toolkits. One can use a 2D bubble chart to show three variables as shown in FIG. 5 or a 3D bubble chart that shows the change in four variables as shown in FIG. 6. Bubble charts are commonly used. However, they are not interactive, do not support drill down, and do not support movement within a category grid row or column.
FIG. 7 is a screen capture illustrating an exemplary “LiveRAC” presentation 70 in accordance with the prior art. The LiveRAC presentation as shown in FIG. 7 allows users to view large amounts of system management data using a matrix of charts. However, LiveRAC does not update charts in real-time and does not indicate potential changes in statistics to users.
FIG. 8 is a screen capture illustrating an exemplary Google™ Motion Chart presentation 80 in accordance with the prior art. One of the views in Google Motion Chart uses bubbles to explore several indicators over time. The location of bubbles in the motion chart can move over time across the entire grid in both the x and y directions. However, it is not possible to drill down into lower level grid categories while maintaining a view of the higher level statistics.
A need therefore exists for an improved method and system for presenting hierarchical time series data. Accordingly, a solution that addresses, at least in part, the above and other shortcomings is desired.